As the Internet continues to expand exponentially, the role of search engines has dramatically increased. The sheer volume of data has quickly become impossible for a human user to manipulate on his or her own without computerized assistance. Thus, the use of search engine technology has become a vital tool in the useful operation of the Internet.
Search engines have made great strides in optimization with respect to the quality of results returned in response to a given query. Currently, existing algorithms typically allow users to identify relevant websites within seconds of submitting a query. Despite these advances, however, search engines have made very little advances with respect to analyzing specific or aggregate user behavior and providing search assistance technologies to existing search engines.
Currently, many search engines take a “one size fits all” approach to providing search assistance technologies. For example, search engines often provide “search suggestions” as a default setting. In this environment, a search engine bombards a user with search suggestions for every query and for every search session. While search suggestions may be useful for some users, however, they may be useless for others. For example, users who are highly adept at utilizing a search engine may find the search suggestions to be a distraction, as opposed to providing additional useful information for shaping subsequent search queries. Thus, the “one size fits all” approach ignores the specific aspects of the searching user.
Often, the user's only recourse is to disable the search assistance technologies. While this may rectify the above-described problem, it is a classic example of burning the house to roast the pig. Specifically, while the intelligent searcher described above may normally not have a need for search suggestions, there may be a time when he or she desires such suggestions, or is unaware of a specific context in which he or she may benefit from one or more search assistance technologies. For example, if the searcher is researching an area with which he or she does not have significant familiarity, search suggestions may prove useful. In this example, a user may execute a plurality of unsuccessful searches, and may eventually concede defeat. In the best-case scenario, he or she may manually turn on search suggestions; however, this outcome is unlikely. Therefore, the user is left executing multiple fruitless searches until, ideally, he or she identifies one or more documents of interest.
As can be seen, the primary shortcoming of the state of the art is the failure to utilize the wealth of metrics obtained from a searching user (or across a group of users in the aggregate) to provide effective search assistance technologies. Thus, there exists a need in the art to provide highly effective search assistance technologies on the basis of a given user's search efficacy and current frustration.